James McCool commited on
Commit
d8888d9
·
1 Parent(s): fa76eda

removing captain ownership from dupe calc for PGA showdown

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Files changed (1) hide show
  1. global_func/predict_dupes.py +9 -4
global_func/predict_dupes.py CHANGED
@@ -227,10 +227,15 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
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  portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
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  # Calculate dupes formula (in progress still)
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- portfolio['dupes_calc'] = ((portfolio['own_product'] + ((portfolio['CPT_Own_percent_rank'] - .50) / 1000) + ((portfolio['Own'] / 6) / (max_salary / 2))) * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (max_salary - portfolio['Own'])) / 100) - ((max_salary - portfolio['salary']) / 100)
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- portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (own_baseline + (Contest_Size / 1000)))
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- portfolio['dupes_calc'] = ((((portfolio['salary'] / (max_salary * 0.98)) - 1)*(max_salary / 10000)) + 1) * portfolio['dupes_calc']
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- portfolio['dupes_calc'] = portfolio['dupes_calc'] * ((portfolio['CPT_Own_percent_rank'] + .50) / (portfolio['Own'] / 110))
 
 
 
 
 
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  # Round and handle negative values
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  portfolio['Dupes'] = np.where(
 
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  portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
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  # Calculate dupes formula (in progress still)
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+ if sport_var == 'GOLF':
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+ portfolio['dupes_calc'] = ((portfolio['own_product'] + ((portfolio['Own'] / 7) / (max_salary / 2))) * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (max_salary - portfolio['Own'])) / 100) - ((max_salary - portfolio['salary']) / 100)
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+ portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (own_baseline + (Contest_Size / 1000)))
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+ portfolio['dupes_calc'] = ((((portfolio['salary'] / (max_salary * 0.98)) - 1)*(max_salary / 10000)) + 1) * portfolio['dupes_calc']
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+ else:
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+ portfolio['dupes_calc'] = ((portfolio['own_product'] + ((portfolio['CPT_Own_percent_rank'] - .50) / 1000) + ((portfolio['Own'] / 6) / (max_salary / 2))) * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (max_salary - portfolio['Own'])) / 100) - ((max_salary - portfolio['salary']) / 100)
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+ portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (own_baseline + (Contest_Size / 1000)))
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+ portfolio['dupes_calc'] = ((((portfolio['salary'] / (max_salary * 0.98)) - 1)*(max_salary / 10000)) + 1) * portfolio['dupes_calc']
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+ portfolio['dupes_calc'] = portfolio['dupes_calc'] * ((portfolio['CPT_Own_percent_rank'] + .50) / (portfolio['Own'] / 110))
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  # Round and handle negative values
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  portfolio['Dupes'] = np.where(